A self-supervised transformer learns to unscramble Feynman integrals for online IBP reduction, delivering bounded memory use on complex two-loop topologies while matching Kira's speed on the hardest cases tested.
Poly-encoders: Transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring
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UNVERDICTED 5representative citing papers
Sentence-BERT adapts BERT with siamese and triplet networks to produce sentence embeddings for efficient cosine-similarity comparisons, cutting computation time from hours to seconds on similarity search while matching BERT accuracy.
Atlas reaches over 42% accuracy on Natural Questions with only 64 examples, outperforming a 540B-parameter model by 3% with 50x fewer parameters.
Contrastive learning trains unsupervised dense retrievers that beat BM25 on most BEIR datasets and support cross-lingual retrieval across scripts.
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.
citing papers explorer
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Learning to Unscramble Feynman Loop Integrals with SAILIR
A self-supervised transformer learns to unscramble Feynman integrals for online IBP reduction, delivering bounded memory use on complex two-loop topologies while matching Kira's speed on the hardest cases tested.
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Sentence-BERT adapts BERT with siamese and triplet networks to produce sentence embeddings for efficient cosine-similarity comparisons, cutting computation time from hours to seconds on similarity search while matching BERT accuracy.
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Atlas: Few-shot Learning with Retrieval Augmented Language Models
Atlas reaches over 42% accuracy on Natural Questions with only 64 examples, outperforming a 540B-parameter model by 3% with 50x fewer parameters.
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Unsupervised Dense Information Retrieval with Contrastive Learning
Contrastive learning trains unsupervised dense retrievers that beat BM25 on most BEIR datasets and support cross-lingual retrieval across scripts.
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Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.